Research Article Open Access

Mass Classification in Digital Mammograms based on Discrete Shearlet Transform

J. Amjath Ali1 and J. Janet2
  • 1 St.Peter’s University, India
  • 2 Dr. M.G.R. University, India

Abstract

The most significant health problem in the world is breast cancer and early detection is the key to predict it. Mammography is the most reliable method to diagnose breast cancer at the earliest. The classification of the two most findings in the digital mammograms, micro calcifications and mass are valuable for early detection. Since, the appearance of the masses are similar to the surrounding parenchyma, the classification is not an easy task. In this study, an efficient approach to classify masses in the Mammography Image Analysis Society (MIAS) database mammogram images is presented. The key features used for the classification is the energies of shearlet decomposed image. These features are fed into SVM classifier to classify mass/non mass images and also benign/malignant. The results demonstrate that the proposed shearlet energy features outperforms the wavelet energy features in terms of accuracy.

Journal of Computer Science
Volume 9 No. 6, 2013, 726-732

DOI: https://doi.org/10.3844/jcssp.2013.726.732

Submitted On: 3 May 2013 Published On: 1 June 2013

How to Cite: Ali, J. A. & Janet, J. (2013). Mass Classification in Digital Mammograms based on Discrete Shearlet Transform. Journal of Computer Science, 9(6), 726-732. https://doi.org/10.3844/jcssp.2013.726.732

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Keywords

  • Wavelet Transform
  • Shearlet Transform
  • SVM Classifier
  • Digital Mammograms
  • Mass Region of Interest